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Adaptive Equivalent Factor-Based Energy Management Strategy for Plug-In Hybrid Electric Buses Considering Passenger Load Variations

Author

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  • Pengxiang Song

    (School of Vehicle and Energy, Yanshan University, Qinhuangdao 066000, China)

  • Wenchuan Song

    (School of Vehicle and Energy, Yanshan University, Qinhuangdao 066000, China)

  • Ao Meng

    (School of Vehicle and Energy, Yanshan University, Qinhuangdao 066000, China)

  • Hongxue Li

    (School of Vehicle and Energy, Yanshan University, Qinhuangdao 066000, China)

Abstract

Energy management strategies (EMSs) are one of the key technologies for the development of plug-in hybrid electric buses (PHEBs). This paper addresses the issue of optimal energy distribution for PHEBs under significant variations in passenger load at different bus stations, which cannot be solved by a single equivalent factor equivalent fuel consumption minimization energy management strategy (ECMS). An adaptive equivalent factor equivalent fuel consumption minimization energy management strategy (A-ECMS) considering passenger load is proposed. First, the powertrain system of the PHEB is modeled, and the accuracy of the model is verified in a simulation environment. Then, the reference SOC descent trajectory of the battery is obtained using a dynamic programming (DP) algorithm, the recursive least squares (RLS) method is employed to identify the passenger load, and the influence of different loads on the state of charge (SOC) trajectory under a single equivalent factor is analyzed. Finally, a genetic algorithm (GA) is used to establish the correspondence between passenger load, bus station, and equivalent factor, enabling the actual SOC to follow the reference SOC descent trajectory, thereby achieving optimal energy distribution. Simulation results demonstrate that the A-ECMS reduces fuel consumption of the PHEB per 100 km by 2.59% and 10.10% compared to the ECMS and rule-based EMS, respectively, validating the effectiveness of the proposed strategy.

Suggested Citation

  • Pengxiang Song & Wenchuan Song & Ao Meng & Hongxue Li, 2024. "Adaptive Equivalent Factor-Based Energy Management Strategy for Plug-In Hybrid Electric Buses Considering Passenger Load Variations," Energies, MDPI, vol. 17(6), pages 1-30, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1283-:d:1352919
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    References listed on IDEAS

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    1. Sun, Chao & Sun, Fengchun & He, Hongwen, 2017. "Investigating adaptive-ECMS with velocity forecast ability for hybrid electric vehicles," Applied Energy, Elsevier, vol. 185(P2), pages 1644-1653.
    2. Yang, Chao & Du, Siyu & Li, Liang & You, Sixong & Yang, Yiyong & Zhao, Yue, 2017. "Adaptive real-time optimal energy management strategy based on equivalent factors optimization for plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 203(C), pages 883-896.
    3. Iris, Çağatay & Lam, Jasmine Siu Lee, 2021. "Optimal energy management and operations planning in seaports with smart grid while harnessing renewable energy under uncertainty," Omega, Elsevier, vol. 103(C).
    4. Zhu, Zhi-Hong & Gao, Zi-You & Zheng, Jian-Feng & Du, Hao-Ming, 2016. "Charging station location problem of plug-in electric vehicles," Journal of Transport Geography, Elsevier, vol. 52(C), pages 11-22.
    5. Oren E. Nahum & Yuval Hadas, 2020. "Multi-Objective Optimal Allocation of Wireless Bus Charging Stations Considering Costs and the Environmental Impact," Sustainability, MDPI, vol. 12(6), pages 1-20, March.
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